From 523fc6c6ca71800b1e96df34bf9bccebd5e11efe Mon Sep 17 00:00:00 2001 From: Tim Connors Date: Tue, 18 Jan 2022 15:13:41 -0500 Subject: [PATCH 1/7] Week 1 --- week1.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/week1.md b/week1.md index e69de29..7e74fa8 100644 --- a/week1.md +++ b/week1.md @@ -0,0 +1,22 @@ +Adam McCann's "An Analysis of the Beatles?" visualization displays songwriting data +about The Beatles discography, specifically the songwriting habits of each member. +It uses color scaling in combination with a scatterplot and a pie chart to link +together the success, time period, and writer of hit Beatles songs. I enjoy how +these three individual visualizations interact with each other. However, I think the +rainbow color scale is a bit difficult to interpret. I would have preferred it to be on +a scale using saturation or luminance instead. + +The "Which Songwriter Has the Largest Vocabulary" section is fascinating to look at, but +a bit unintuitive. A song's position on the y-axis is determined by how many unique words +the song has, but the visualization does not define what a "unique" word is. The songs +to be organized on the x-axis (within each individual songwriter's chart) in alphabetical +order. This information is not very meaningful, and I think it would make more sense to +instead organize it by the release date of the song, which might reveal some trends in +the data. + +Overall, the visualization is very interactive and effectively displays a lot of data. +It highlights specific graphics when mousing over them and displays text boxes with +further information. It also includes a search function to find specific songs, which +makes the visualization a lot easier to navigate through. I would have liked to see +a bit more polish in displaying the data meaningfully, but McCann's visualization +does a very effective job at visualizing Beatles songwriting data. \ No newline at end of file From 7396e234eba475740da25bbee8d266da3dcb4575 Mon Sep 17 00:00:00 2001 From: Tim Connors Date: Tue, 18 Jan 2022 15:16:38 -0500 Subject: [PATCH 2/7] Week 1 Updated Added link to visualization --- week1.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/week1.md b/week1.md index 7e74fa8..a7a4f4d 100644 --- a/week1.md +++ b/week1.md @@ -1,3 +1,5 @@ +[Link to visualization](http://duelingdata.blogspot.com/2016/01/the-beatles.html) + Adam McCann's "An Analysis of the Beatles?" visualization displays songwriting data about The Beatles discography, specifically the songwriting habits of each member. It uses color scaling in combination with a scatterplot and a pie chart to link From d45fa74c69e943a2a8b92d5f51dd9ea0d90f3b5e Mon Sep 17 00:00:00 2001 From: Tim Connors Date: Tue, 18 Jan 2022 16:17:45 -0500 Subject: [PATCH 3/7] Week 1 Updated --- week1.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/week1.md b/week1.md index a7a4f4d..4e46adf 100644 --- a/week1.md +++ b/week1.md @@ -10,15 +10,15 @@ a scale using saturation or luminance instead. The "Which Songwriter Has the Largest Vocabulary" section is fascinating to look at, but a bit unintuitive. A song's position on the y-axis is determined by how many unique words -the song has, but the visualization does not define what a "unique" word is. The songs -to be organized on the x-axis (within each individual songwriter's chart) in alphabetical -order. This information is not very meaningful, and I think it would make more sense to -instead organize it by the release date of the song, which might reveal some trends in -the data. +the song has, but the visualization does not define what a "unique" word is. The songs seem +to be organized on the x-axis (within each individual songwriter's chart) in alphabetically. +This information is not very meaningful, and I think it would make more sense to +instead organize it by the release date of the song, which might reveal some more +interesting trends in the data. Overall, the visualization is very interactive and effectively displays a lot of data. It highlights specific graphics when mousing over them and displays text boxes with -further information. It also includes a search function to find specific songs, which +further information. It also includes a search feature to find specific songs, which makes the visualization a lot easier to navigate through. I would have liked to see a bit more polish in displaying the data meaningfully, but McCann's visualization does a very effective job at visualizing Beatles songwriting data. \ No newline at end of file From 1b7903001cfe6935c3c6778593654e82cf3387d9 Mon Sep 17 00:00:00 2001 From: Tim Connors Date: Tue, 18 Jan 2022 16:19:16 -0500 Subject: [PATCH 4/7] Week 1 Updated --- week1.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/week1.md b/week1.md index 4e46adf..d463836 100644 --- a/week1.md +++ b/week1.md @@ -11,7 +11,7 @@ a scale using saturation or luminance instead. The "Which Songwriter Has the Largest Vocabulary" section is fascinating to look at, but a bit unintuitive. A song's position on the y-axis is determined by how many unique words the song has, but the visualization does not define what a "unique" word is. The songs seem -to be organized on the x-axis (within each individual songwriter's chart) in alphabetically. +to be organized on the x-axis (within each individual songwriter's chart) alphabetically. This information is not very meaningful, and I think it would make more sense to instead organize it by the release date of the song, which might reveal some more interesting trends in the data. From 8db53516656f4cda44a726bf454e817756967c04 Mon Sep 17 00:00:00 2001 From: Tim Connors Date: Sun, 23 Jan 2022 21:15:54 -0500 Subject: [PATCH 5/7] Week 2 --- week2.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/week2.md b/week2.md index e69de29..46c0898 100644 --- a/week2.md +++ b/week2.md @@ -0,0 +1,9 @@ +[Link to visualization](https://nba3d.peterbeshai.com/) + +After Professor Harrison showed some of Peter Beshai's NBA visualizations in class, I was curious to delve further into them as an avid NBA fan. In particular, I found some of his 3D visualizations and decided to explore them. They center around player career totals in things like points, assists, and rebounds. + +It is interesting how Beshai uses the added dimension to display further information about data points. He has tools for laying out the data by teams in columns and geographically. There is also a "step" format, where the cylinders are put together in a grid and sorted by value. All of the formats are fascinating to look at, but some are more informative than others. There is a cluster-type format that is very elegant but seems inferior to the grid format in comparing stats across players. There are also formats to divide players by teams in a grid, but I feel that the geographical layout is a lot more intuitive. It is much easier to find a team by looking for their home city than by searching through a grid sorted in alphabetical order. + +Additionally, I feel that Beshai could have made some use of color instead of having all the cylinders be gray. Perhaps there could have been some toggle-able feature that would enable a color scale based on a player's height, seasons played, or time period in which they played. + +Overall, I found it fascinating to explore a 3D visualization. It helps provide an extra sense of scale to the data, and really makes me appreciate the statistical outliers more. \ No newline at end of file From de9fb66313c238820de3c6fca5fc2f0d4219e901 Mon Sep 17 00:00:00 2001 From: Tim Connors Date: Mon, 31 Jan 2022 01:02:05 -0500 Subject: [PATCH 6/7] Week 3 --- week3.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/week3.md b/week3.md index e69de29..728efdf 100644 --- a/week3.md +++ b/week3.md @@ -0,0 +1,7 @@ +[Link to visualization](https://blog.ebemunk.com/a-visual-look-at-2-million-chess-games/) + +I am fan of chess, and the turn-based, methodical nature of the game makes analytics very wide-spread in the community. The "Opening Tree" visualization featured in the above link is one I found particularly fascinating. It displays, in a pie chart-esque format, the most common five opening moves from a database of over two million chess games. + +While pie charts in general are not very effective at displaying information meaningfully, this one has some added features which makes it a bit more intuitive and impressive. When mousing over a particular slice, it displays a tooltip with a variety of useful information, including the percentage of games featuring the previous move it was played in. It also plays the move on the board in the center, making the chart more interactive and aiding in visualization. Another nice feature is that it displays a lighter shade of color for moves played with the white pieces and a darker shade for moved played with the black pieces. I also found the splitting of the chart into different colors based on the first move to help in digesting the data. + +I think what makes the visualization effective is that the creator does everything they can to minimize the weaknesses of pie charts with these additional features. In fact, I think a pie chart might have been used by necessity and not by choice. I cannot think of another chart or data structure that could display all this information at once. It has made me reconsider my previous position that using a pie chart leads to a bad visualization by default. \ No newline at end of file From 43aec48caaf57c4cb7f44ea0f642c9008c5908a0 Mon Sep 17 00:00:00 2001 From: Tim Connors Date: Mon, 7 Feb 2022 10:04:51 -0500 Subject: [PATCH 7/7] Week 4 --- week4.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/week4.md b/week4.md index e69de29..81c5725 100644 --- a/week4.md +++ b/week4.md @@ -0,0 +1,7 @@ +[Link to visualization](https://whydocatsanddogs.com/) + +"Why do cats & dogs ..?" visualizes the most commonly searched terms asking about the behavior of cats and dogs. There are two separate visualizations for dogs and cats, both following the same style. There is a variety of bubbles laid out, with the word after the prompt "Why do dogs/cats" or "Why does my dog/cat" in the center of each bubble. Subsequent words in the search branch off from that main word inside each bubble. The data is taken from Google Trends. + +The visualization is extremely pleasing to look at. It has a great color palette and is fun to explore. The search bar feature allows you to convieniently search for terms like in a standard search engine. The tooltip that comes up when you mouse over a node offers a written view of it, and clicking on the node redirects you to a Google search with the inputted inquiry. + +For exploratory purposes, the visualization is extremely effective. It would be interesting to see how the author could incorporate more expositive features, such as using color scales to furthur express how common a search term is. There are supplemental visualizations which do offer some more general data comparisons, but I would like to see how an integrated way of measuring the nodes against each other would look, beyond just the size of the bubbles. \ No newline at end of file